The practice of capturing color images with digital imaging devices or systems, such as a digital camera or color scanner, is widely spreading. Currently, these digital devices comprise a charge-coupled device or complementary metaloxide-semiconductor (CCD/CMOS) sensor array with a set of filters before it. Ideally, these digital imaging devices capture color images in a substantially accurate and aesthetically pleasing manner.
There are many criteria which are utilized in the design and production of color imaging devices. An important consideration is the ability of the device to deliver color signals which can be used to create high quality color reproductions. To quantify the color quality capability of such devices, it is important to determine how the device's response to color stimuli corresponds to that of a human. Through psychophysical experiments, the Commission Internationale de l'Eclairage (CIE) has generated a standard set of color matching functions for the standard observer, representing the average human visual response to color. As the human eye has three types of cones, each with a different spectral sensitivity signature, there are three standard function specified by the CIE. Accordingly, most color imaging devices are set up with three channels and the spectral sensitivities in these imaging devices are initially designed to attempt to mimic the human visual system.
The spectral sensitivity functions for color imaging device channels should satisfy the Luther condition, that is the channel spectral sensitivities need not be exact duplicates of the CIE color-matching functions described above, but need to be a nonsingular transformation of them. In practice, it is not always possible to manufacture filters for imaging devices that satisfy the Luther condition, often due to the physical limitations of fabricating process. Measurement noise also plays an important role and will degrade the color accuracy even when spectral sensitivity curves fulfill the Luther condition.
Accordingly, a measure of goodness or quality factor for evaluating, optimizing, and/or designing spectral sensitivity curves for color imaging devices is desirable. Unfortunately, developing methods and/or systems for obtaining a measure of goodness or a quality factor has been challenging.
One quality factor, known as the Q-factor, for evaluating and designing spectral sensitivity functions is disclosed in H. E. J. Neugebauer, “Quality factor for filters whose spectral transmittances are different from color mixture curves, and its application to color photography,” J. Opt. Soc. Am. Vol. 46, No. 10, pg. 821–824 (1956), which is herein incorporated by reference. A major disadvantage of the Q-factor is that it is designed to evaluate only single spectral sensitivities. Thus, it is easily possible to design an imaging device where all channels associate with high Q-factors, and still the system delivers color signals which cannot be used in creating high quality color reproductions. The trivial example of such a system is a three-channel device where all three channels are made with small deviations from the same spectral sensitivity signature. Although all three show high Q-factor, the result is a nearly monochrome image.
Another quality factor, known as the μ-factor, for evaluating and designing spectral sensitivity functions is disclosed in P. L. Vora and H. J. Trussell, “Measure of goodness of a set of color-scanning filters”, J. Opt. Soc. Am. A, Vol. 10, No. 7, pg. 1499–1503 (1993), which is herein incorporated by reference. The μ-factor is a quality factor which evaluates an arbitrary number of device channel sensitivity functions as an ensemble. More specifically, the μ-factor describes the difference between the orthonormal subspaces of the CIE color matching functions and the spectral sensitivity space. Generally, a μ-factor approximately equal to one is an indication of an imaging device expected to deliver high quality color reproduction. Unfortunately, some color imaging devices with a poor μ-factor can still generate a good image reproduction. Vora and Trussell have thus introduced a metric which does not have a straight forward correlation with expected image quality. Another problem with the μ-factor is that it does not penalize systems that by virtue of channel sensitivity choices tend to amplify system noise. Such noise is unavoidable in real systems and a system designer needs to carefully control it so as to avoid swamping the eventual color reproduction.
CIELAB color space is a non-linear transfromation of the CIE color matching functions used as an approximation to a perceptually uniform color space. A figure of merit for color scanners, based on locally-linearized CIELAB space, having an incorporated model for measurement of noise is present in G. Sharma et al. “Figure of merit for color scanners,” IEEE Trans. Image Processing, Vol. 6, No. 7, pg. 990–1001 (1997) which is herein incorporated by reference. The new figure has a high degree of perceptual relevance and also accounts for noise performance of sets of channel sensitivities. The noise aspect of Sharma's metric is, however completely independent of the input stimulus and thus is limited in its usefulness.